BayesS5: Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)

In p >> n settings, full posterior sampling using existing Markov chain Monte Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities).

Version: 1.31
Depends: R (≥ 3.3)
Imports: Matrix, stats, snowfall, abind
Published: 2018-10-26
Author: Minsuk Shin and Ruoxuan Tian
Maintainer: Minsuk Shin <minsuk000 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://arxiv.org/pdf/1507.07106.pdf
NeedsCompilation: no
CRAN checks: BayesS5 results

Downloads:

Reference manual: BayesS5.pdf
Package source: BayesS5_1.31.tar.gz
Windows binaries: r-devel: BayesS5_1.31.zip, r-devel-gcc8: BayesS5_1.31.zip, r-release: BayesS5_1.31.zip, r-oldrel: BayesS5_1.31.zip
OS X binaries: r-release: BayesS5_1.31.tgz, r-oldrel: BayesS5_1.31.tgz
Old sources: BayesS5 archive

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